1. Technical Field
The present disclosure relates to image segmentation and, more specifically, to a fluid dynamics approach to image segmentation.
2. Discussion of Related Art
Image segmentation relates to the field of processing digital images to accurately distinguish between multiple objects that appear within the image. Image segmentation may be performed for either two-dimensional images or three-dimensional images, still images or moving images, and may be performed for all forms of images, regardless of their modality. While image segmentation may be performed on photographic images, image segmentation is particularly useful in the field of medical imaging. For example, image segmentation may be performed on CT or MR images to distinguish between various organs and other anatomical structures. By accurately distinguishing between anatomical structures within medical images, other medical image processing techniques may be more accurately performed, for example, to detect polyps, lesions, tumors from various portions of the body. Accordingly, image segmentation may play an important role in computer-aided detection of various diseases.
There are many techniques for performing image segmentation. Many techniques involve prompting a user to inspect acquired image data and to provide one or more seed locations that the user knows to be inside of the region to be segmented and/or outside of the region to be segmented. Using this information, image segmentation may be automatically performed by an image processing system to distinguish between image pixels/voxels that are inside the region to be segmented and pixels/voxels that are outside of the region to be segmented.
According to one simple approach for image segmentation, the user may provide a seed location that is understood to be inside the region to be segmented. A region growing algorithm may then be performed wherein each pixel/voxel adjacent to the selected seed is analyzed to determine whether it represents a change of intensity, with respect to the seed pixel/voxel, in excess of a predetermined threshold. So long as the adjacent pixel/voxel does not represent a sufficiently large change of intensity, that pixel/voxel may be considered to be part of the region to be segmented and that pixel/voxel may then be considered a seed for future iterations of this recursive process. The process may end when all pixels/voxels adjacent to the pixels/voxels that are considered to be part of the region to be segmented represent a change of intensity that is in excess of the predetermined threshold.
However, such techniques are not without problems. For example, where the boundaries of the region to be segmented include one or more points of weakness, for example, due to insufficient contrast between the region and its surrounding tissue, the growing algorithm may escape the boundary and generate an over-inclusive segmentation. Moreover, areas of excessive contrast within the region to be segmented may result in the growing algorithm stopping prematurely and thus generating an under-inclusive segmentation. Image noise may also adversely affect region growing techniques by obfuscating actual boundaries and creating the appearance of boundaries where none exist.
Other approaches for segmentation include the Random Walker algorithm, described in detail below. This method overcomes difficulties associated with region growing, but still has disadvantages such as when the image contains very weak boundaries or intersecting objects such as vessels.